Previous work attempt to integrate type information into the entity linking task mostly by jointly modeling named entity recognition and entity linking. Specifically, a line of work [1]}, [2]}, [3]} jointly model entity linking and named entity recognition to capture the mutual dependency between them using structured CRF. These methods mainly differ in the design of hand-engineered features. Recently, [4]} martins-etal-2019-joint perform multi-task learning using learned features by extending Stack-LSTM [5]}. However, all of these work rely on extensive annotation of the type of mentions which are difficult to obtain on most of the entity linking datasets. In contrast, based on the assumption that “context consistency is a strong proxy for type compatibility" from [6]} zhou-etal-2018-zero, we propose to model a mention's immediate context using BERT [7]} to capture its contextual latent entity type information.
